Smart Resource Planning for Live Migration in Edge Computing for Industrial Scenario

Govindaraj, Keerthana; John, Jibin P; Artemenko, Alexander; Kirstaedter, Andreas

doi:10.1109/mobilecloud.2019.00012

Cited by 7 publications

(3 citation statements)

References 22 publications

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“…As a solution to this challenge, research has strived to propose adaptive mechanisms based on migrating services between nodes. Govindaraj et al [12] present an approach to perform smart resource allocation allowing them to achieve live migrations on demand. Since performing a migration is an expensive task, the solution tries to minimize the migrations required while maintaining the round trip time for each device under a certain threshold.…”

Section: Adaptive Techniquesmentioning

confidence: 99%

Adaptive Management of Volatile Edge Systems at Runtime With Satisfiability

Avasalcai

Tsigkanos

Dustdar

2021

ACM Trans. Internet Technol.

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Edge computing offers the possibility of deploying applications at the edge of the network. To take advantage of available devices’ distributed resources, applications often are structured as microservices, often having stringent requirements of low latency and high availability. However, a decentralized edge system that the application may be intended for is characterized by high volatility, due to devices making up the system being unreliable or leaving the network unexpectedly. This makes application deployment and assurance that it will continue to operate under volatility challenging. We propose an adaptive framework capable of deploying and efficiently maintaining a microservice-based application at runtime, by tackling two intertwined problems: (i) finding a microservice placement across device hosts and (ii) deriving invocation paths that serve it. Our objective is to maintain correct functionality by satisfying given requirements in terms of end-to-end latency and availability, in a volatile edge environment. We evaluate our solution quantitatively by considering performance and failure recovery.

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Section: Adaptive Techniquesmentioning

confidence: 99%

Adaptive Management of Volatile Edge Systems at Runtime With Satisfiability

Avasalcai

Tsigkanos

Dustdar

2021

ACM Trans. Internet Technol.

View full text Add to dashboard Cite

show abstract

“…Although companies are aware of the potential value of raw data, the affordable storage cost in combination with lack of local processing power and out-of-the-box software solutions leads to the implementation of large (raw) data lakes and warehouses instead of an increase in insight information [18]. EC addresses the requirements set by upcoming industrial systems [19]. It enables decentral implementations by providing local processing and communication capabilities that allow new types of data-driven solutions.…”

Section: Relevance In Manufacturingmentioning

confidence: 99%

“…The scoring model was designed in such a way that 'must' criteria are directly included, hence the significant difference between the scores. The sum of the scores of all five criteria results in an overall edge level recommendation: Cloud (0-3), Fog (4-11), Edge (12)(13)(14)(15)(16)(17)(18)(19) or smart thing/on-device edge (20+). The levels are displayed in Fig.…”

Section: Operationalizationmentioning

confidence: 99%